% Encoding: UTF-8

% Active Learning Surveys

@techreport{settles2009active,
Author = {Burr Settles},
Institution = {University of Wisconsin--Madison},
Number = {1648},
Title = {Active Learning Literature Survey},
Type = {Computer Sciences Technical Report},
Year = {2009},
url = {http://burrsettles.com/pub/settles.activelearning.pdf}
}

@phdthesis{Tong2001,
  title={Active learning: theory and applications},
  school = {Stanford University},
  author={Simon Tong},
  year={2001},
  url = {http://www.robotics.stanford.edu/~stong/papers/tong_thesis.pdf}
}

@ARTICLE{humanOut, 
author={B. Shahriari and K. Swersky and Z. Wang and R. P. Adams and N. de Freitas}, 
journal={Proceedings of the IEEE}, 
title={Taking the Human Out of the Loop: A Review of Bayesian Optimization}, 
year={2016}, 
volume={104}, 
number={1}, 
pages={148-175}, 
keywords={Bayes methods;Big Data;optimisation;storage allocation;Bayesian optimization;human productivity;product quality;storage architecture;large-scale heterogeneous computing;massive complex software system;Big data application;Big data;Bayes methods;Linear programming;Decision making;Design of experiments;Optimization;Genomes;Statistical analysis;decision making;design of experiments;optimization;response surface methodology;statistical learning;genomic medicine;Decision making;design of experiments;optimization;response surface methodology;statistical learning}, 
doi={10.1109/JPROC.2015.2494218}, 
ISSN={0018-9219}, 
month={Jan},
}

@article{gpucb,
 author = {Auer, Peter},
 title = {Using Confidence Bounds for Exploitation-Exploration Trade-Offs},
 year = {2003},
 month = {Mar},
 publisher = {JMLR.org},
 volume = {3},
 number = {null},
 issn = {1532-4435},
 journal = {J. Mach. Learn. Res.},
 pages = {397–422},
 numpages = {26},
 keywords = {exploitation-exploration, linear value function, bandit problem, online Learning, reinforcement learning}
}


@ARTICLE{gpucbBounds,
       author = {Srinivas, Niranjan and Krause, Andreas and Kakade, Sham M. and
         Seeger, Matthias},
        title = {Gaussian Process Optimization in the Bandit Setting: No Regret and Experimental Design},
      journal = {arXiv e-prints},
     keywords = {Computer Science - Machine Learning},
         year = "2009",
        month = "Dec",
          eid = {arXiv:0912.3995},
        pages = {arXiv:0912.3995},
archivePrefix = {arXiv},
       eprint = {0912.3995},
 primaryClass = {cs.LG},
       adsurl = {https://ui.adsabs.harvard.edu/abs/2009arXiv0912.3995S},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}

@ARTICLE{nandoBOLoop, 
author={B. {Shahriari} and K. {Swersky} and Z. {Wang} and R. P. {Adams} and N. {de Freitas}}, 
journal={Proceedings of the IEEE}, 
title={Taking the Human Out of the Loop: A Review of Bayesian Optimization}, 
year={2016}, 
volume={104}, 
number={1}, 
pages={148-175},
}

@inproceedings{Snoek2012,
 author = {Snoek, Jasper and Larochelle, Hugo and Adams, Ryan P.},
 title = {Practical Bayesian Optimization of Machine Learning Algorithms},
 booktitle = {Proceedings of the 25th International Conference on Neural Information Processing Systems - Volume 2},
 series = {NIPS'12},
 year = {2012},
 location = {Lake Tahoe, Nevada},
 pages = {2951--2959},
 numpages = {9},
 url = {http://dl.acm.org/citation.cfm?id=2999325.2999464},
 acmid = {2999464},
 publisher = {Curran Associates Inc.},
 address = {USA},
}

@article{nandoBOtut,
author = {Brochu, Eric and M. Cora, Vlad and De Freitas, Nando},
year = {2010},
month = {12},
title = {A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning},
volume = {abs/1012.2599},
journal = {CoRR}
}

@inproceedings{NIPS2011_4443,
author = {Bergstra, James and Bardenet, R\'{e}mi and Bengio, Yoshua and K\'{e}gl, Bal\'{a}zs},
title = {Algorithms for Hyper-Parameter Optimization},
year = {2011},
isbn = {9781618395993},
publisher = {Curran Associates Inc.},
address = {Red Hook, NY, USA},
booktitle = {Proceedings of the 24th International Conference on Neural Information Processing Systems},
pages = {2546–2554},
numpages = {9},
location = {Granada, Spain},
series = {NIPS’11}
}

@inproceedings{Bergstra,
author = {Bergstra, J. and Yamins, D. and Cox, D. D.},
title = {Making a Science of Model Search: Hyperparameter Optimization in Hundreds of Dimensions for Vision Architectures},
year = {2013},
publisher = {JMLR.org},
booktitle = {Proceedings of the 30th International Conference on International Conference on Machine Learning - Volume 28},
pages = {I–115–I–123},
numpages = {9},
location = {Atlanta, GA, USA},
series = {ICML’13},
url = {http://dl.acm.org/citation.cfm?id=3042817.3042832},
acmid = {3042832},
}

@ARTICLE{peterTutBO,
       author = {Frazier, Peter I.},
        title = "{A Tutorial on Bayesian Optimization}",
      journal = {arXiv e-prints},
     keywords = {Statistics - Machine Learning, Computer Science - Machine Learning, Mathematics - Optimization and Control},
         year = "2018",
        month = "Jul",
          eid = {arXiv:1807.02811},
        pages = {arXiv:1807.02811},
archivePrefix = {arXiv},
       eprint = {1807.02811},
 primaryClass = {stat.ML},
       adsurl = {https://ui.adsabs.harvard.edu/abs/2018arXiv180702811F},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}

@article{görtler2019a,
  author = {Görtler, Jochen and Kehlbeck, Rebecca and Deussen, Oliver},
  url = {https://distill.pub/2019/visual-exploration-gaussian-processes/},
  title = {A Visual Exploration of Gaussian Processes},
  journal = {Distill},
  year = {2019},
  note = {https://distill.pub/2019/visual-exploration-gaussian-processes},
  doi = {10.23915/distill.00017}
}

@InCollection{Rasmussen2004,
  author    = {Carl Edward Rasmussen},
  title     = {Gaussian Processes in Machine Learning},
  booktitle = {Advanced Lectures on Machine Learning},
  publisher = {Springer Berlin Heidelberg},
  year      = {2004},
  pages     = {63--71},
  doi       = {10.1007/978-3-540-28650-9_4},
  url       = {http://www.gaussianprocess.org/gpml/chapters/RW.pdf}
}

@article{thompTut,
author = {Daniel J. Russo and Benjamin Van Roy and Abbas Kazerouni and Ian Osband and Zheng Wen},
url = {http://dx.doi.org/10.1561/2200000070},
year = {2018},
volume = {11},
journal = {Foundations and Trends® in Machine Learning},
title = {A Tutorial on Thompson Sampling},
doi = {10.1561/2200000070},
issn = {1935-8237},
number = {1},
pages = {1-96}
}

@incollection{BOwtGD,
title = {Bayesian Optimization with Gradients},
author = {Wu, Jian and Poloczek, Matthias and Wilson, Andrew G and Frazier, Peter},
booktitle = {Advances in Neural Information Processing Systems 30},
editor = {I. Guyon and U. V. Luxburg and S. Bengio and H. Wallach and R. Fergus and S. Vishwanathan and R. Garnett},
pages = {5267--5278},
year = {2017},
publisher = {Curran Associates, Inc.},
url = {http://papers.nips.cc/paper/7111-bayesian-optimization-with-gradients.pdf}
}

@article{mockusEI,
author = {B. Mockus, J and Mockus, Linas},
year = {1991},
month = {07},
pages = {157-172},
title = {Bayesian approach to global optimization and application to multiobjective and constrained problems},
volume = {70},
journal = {Journal of Optimization Theory and Applications},
doi = {10.1007/BF00940509}
}

@article{scikit,
 title={Scikit-learn: Machine Learning in {P}ython},
 author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
         and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.
         and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
         Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
 journal={Journal of Machine Learning Research},
 volume={12},
 pages={2825--2830},
 year={2011}
}

@ARTICLE{yelpBO,
       author = {Wang, Jialei and Clark, Scott C. and Liu, Eric and
         Frazier, Peter I.},
        title = {Parallel Bayesian Global Optimization of Expensive Functions},
      journal = {arXiv e-prints},
     keywords = {Statistics - Machine Learning, Mathematics - Optimization and Control},
         year = {2016},
        month = {Feb},
          eid = {arXiv:1602.05149},
        pages = {arXiv:1602.05149},
archivePrefix = {arXiv},
       eprint = {1602.05149},
 primaryClass = {stat.ML},
       adsurl = {https://ui.adsabs.harvard.edu/abs/2016arXiv160205149W},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}

@article{letham2019,
author = "Letham, Benjamin and Karrer, Brian and Ottoni, Guilherme and Bakshy, Eytan",
doi = "10.1214/18-BA1110",
fjournal = "Bayesian Analysis",
journal = "Bayesian Anal.",
month = "06",
number = "2",
pages = "495--519",
publisher = "International Society for Bayesian Analysis",
title = "Constrained Bayesian Optimization with Noisy Experiments",
url = "https://doi.org/10.1214/18-BA1110",
volume = "14",
year = "2019"
}

@inproceedings{sensorBO,
 author = {Garnett, R. and Osborne, M. A. and Roberts, S. J.},
 title = {Bayesian Optimization for Sensor Set Selection},
 booktitle = {Proceedings of the 9th ACM/IEEE International Conference on Information Processing in Sensor Networks},
 series = {IPSN '10},
 year = {2010},
 isbn = {978-1-60558-988-6},
 location = {Stockholm, Sweden},
 pages = {209--219},
 numpages = {11},
 url = {http://doi.acm.org/10.1145/1791212.1791238},
 doi = {10.1145/1791212.1791238},
 acmid = {1791238},
 publisher = {ACM},
 address = {New York, NY, USA},
 keywords = {Bayesian methods, Gaussian processes, experimental design, global optimization, sampling design, sensor networks, sensor selection, spatial learning},
}

@inproceedings{multiACQ,
 author = {Hoffman, Matthew and Brochu, Eric and de Freitas, Nando},
 title = {Portfolio Allocation for Bayesian Optimization},
 booktitle = {Proceedings of the Twenty-Seventh Conference on Uncertainty in Artificial Intelligence},
 series = {UAI'11},
 year = {2011},
 isbn = {978-0-9749039-7-2},
 location = {Barcelona, Spain},
 pages = {327--336},
 numpages = {10},
 url = {http://dl.acm.org/citation.cfm?id=3020548.3020587},
 acmid = {3020587},
 publisher = {AUAI Press},
 address = {Arlington, Virginia, United States},
}

@inproceedings{NNbasedBO,
 author = {Snoek, Jasper and Rippel, Oren and Swersky, Kevin and Kiros, Ryan and Satish, Nadathur and Sundaram, Narayanan and Patwary, Md. Mostofa Ali and Prabhat, Prabhat and Adams, Ryan P.},
 title = {Scalable Bayesian Optimization Using Deep Neural Networks},
 booktitle = {Proceedings of the 32Nd International Conference on International Conference on Machine Learning - Volume 37},
 series = {ICML'15},
 year = {2015},
 location = {Lille, France},
 pages = {2171--2180},
 numpages = {10},
 url = {http://dl.acm.org/citation.cfm?id=3045118.3045349},
 acmid = {3045349},
 publisher = {JMLR.org},
} 

@article{hyperband,
author  = {Liam Li and Kevin Jamieson and Giulia DeSalvo and Afshin Rostamizadeh and Ameet Talwalkar},
title = {Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization},
year  = {2018},
URL = {http://www.jmlr.org/papers/volume18/16-558/16-558.pdf},
journal = {Journal of Machine Learning Research},
pages = {1-52},
volume  = {18-185}
}

@InProceedings{largeBO,
  title =    {Fast Bayesian Optimization of Machine Learning Hyperparameters on Large Datasets},
  author =   {Aaron Klein and Stefan Falkner and Simon Bartels and Philipp Hennig and Frank Hutter},
  booktitle =    {Proceedings of the 20th International Conference on Artificial Intelligence and Statistics},
  pages =    {528--536},
  year =   {2017},
  editor =   {Aarti Singh and Jerry Zhu},
  volume =   {54},
  series =   {Proceedings of Machine Learning Research},
  address =    {Fort Lauderdale, FL, USA},
  month =    {20--22 Apr},
  publisher =    {PMLR},
  pdf =    {http://proceedings.mlr.press/v54/klein17a/klein17a.pdf},
  url =    {http://proceedings.mlr.press/v54/klein17a.html},
  abstract =   {Bayesian optimization has become a successful tool for hyperparameter optimization of machine learning algorithms, such as support vector machines or deep neural networks. Despite its success, for large datasets, training and validating a single configuration often takes hours, days, or even weeks, which limits the achievable performance. To accelerate hyperparameter optimization, we propose a generative model for the validation error as a function of training set size, which is learned during the optimization process and allows exploration of preliminary configurations on small subsets, by extrapolating to the full dataset.  We construct a Bayesian optimization procedure, dubbed FABOLAS, which models loss and training time as a function of dataset size and automatically trades off high information gain about the global optimum against computational cost. Experiments optimizing support vector machines and deep neural networks show that FABOLAS often finds high-quality solutions 10 to 100 times faster than other state-of-the-art Bayesian optimization methods or the recently proposed bandit strategy Hyperband.}
}

@inproceedings{SafeExplore,
 author = {Sui, Yanan and Gotovos, Alkis and Burdick, Joel W. and Krause, Andreas},
 title = {Safe Exploration for Optimization with Gaussian Processes},
 booktitle = {Proceedings of the 32Nd International Conference on International Conference on Machine Learning - Volume 37},
 series = {ICML'15},
 year = {2015},
 location = {Lille, France},
 pages = {997--1005},
 numpages = {9},
 url = {http://dl.acm.org/citation.cfm?id=3045118.3045225},
 acmid = {3045225},
 publisher = {JMLR.org},
}

@article{thompson,
    author = {Thompson, William R},
    title = {On The Likelihood That One Unknown Probability Exceeds Another In View Of The Evidence Of Two Samples},
    journal = {Biometrika},
    volume = {25},
    number = {3-4},
    pages = {285-294},
    year = {1933},
    month = {12},
    issn = {0006-3444},
    doi = {10.1093/biomet/25.3-4.285},
    url = {https://doi.org/10.1093/biomet/25.3-4.285},
}

@article{goldKridge,
   author = "Krige, D.G.",
   title = "A statistical approach to some basic mine valuation problems on the Witwatersrand ", 
   journal= "Journal of the Southern African Institute of Mining and Metallurgy",
   year = "1951",
   volume = "52",
   number = "6",
   pages = "119-139",
   url = "https://journals.co.za/content/saimm/52/6/AJA0038223X_4792",
   publisher = "Southern African Institute of Mining and Metallurgy",
   issn = "0038-223X",
   type = "Journal Article",
   language = "English",
   abstract = "Certain fundamental concepts in the application of statistics to mine valuation on the Witwatersrand are discussed, and general conclusions are drawn regarding the application of the lognormal curve to the frequency distribution of gold values. An indication is given of the reliability of present valuation methods on the Rand. It is shown that the existing over- and under-valuation of blocks of ore listed as high-grade and low-grade, respectively, can be explained statistically. Suggestions are made for the elimination of such errors and for the improvement of the general standard of mine valuation by the use of statistical theory.",
  }

